






"""simple docstring"""





import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links



def   a    (     __UpperCAmelCase      :    Union[str, Any]      ,   __UpperCAmelCase      :    List[str]=7  )    ->							Optional[Any]:
						__magic_name__:	Optional[Any]							=       None
						if token is not None:
												__magic_name__:	Union[str, Any]							=       {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

						# The id of a workflow (not of a workflow run)
						__magic_name__:	Optional[Any]							=       """636036"""

						__magic_name__:	Tuple							=       f'https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'
						# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
						url += f'?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'

						__magic_name__:	Union[str, Any]							=       requests.get(__UpperCAmelCase      ,   headers=__UpperCAmelCase  ).json()

						return result["workflow_runs"]



def   a    (     __UpperCAmelCase      :    Tuple  )    ->							int:
						__magic_name__:	Any							=       get_daily_ci_runs(__UpperCAmelCase  )
						__magic_name__:	int							=       None
						for workflow_run in workflow_runs:
												if workflow_run["status"] == "completed":
																		__magic_name__:	List[str]							=       workflow_run["""id"""]
																		break

						return workflow_run_id



def   a    (     __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    Optional[int]      ,   __UpperCAmelCase      :    Tuple  )    ->							Any:
						__magic_name__:	List[str]							=       get_last_daily_ci_runs(__UpperCAmelCase  )
						if workflow_run_id is not None:
												__magic_name__:	Any							=       get_artifacts_links(worflow_run_id=__UpperCAmelCase      ,   token=__UpperCAmelCase  )
												for artifact_name in artifact_names:
																		if artifact_name in artifacts_links:
																								__magic_name__:	List[str]							=       artifacts_links[artifact_name]
																								download_artifact(
																								    artifact_name=__UpperCAmelCase      ,   artifact_url=__UpperCAmelCase      ,   output_dir=__UpperCAmelCase      ,   token=__UpperCAmelCase  )



def   a    (     __UpperCAmelCase      :    Any      ,   __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    str  )    ->							List[Any]:
						get_last_daily_ci_artifacts(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )

						__magic_name__:	int							=       {}
						for artifact_name in artifact_names:
												__magic_name__:	List[Any]							=       os.path.join(__UpperCAmelCase      ,   f'{artifact_name}.zip'  )
												if os.path.isfile(__UpperCAmelCase  ):
																		__magic_name__:	Union[str, Any]							=       {}
																		with zipfile.ZipFile(__UpperCAmelCase  ) as z:
																								for filename in z.namelist():
																														if not os.path.isdir(__UpperCAmelCase  ):
																																				# read the file
																																				with z.open(__UpperCAmelCase  ) as f:
																																										__magic_name__:	int							=       f.read().decode("""UTF-8"""  )

						return results











'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCAmelCase        =							logging.get_logger("transformers.models.speecht5")


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ,       lowercase_       :  Dict   ,       lowercase_       :  Any   ):
       """simple docstring"""




       hf_model.apply_weight_norm()

       a_						     =			checkpoint['input_conv.weight_g']
       a_						     =			checkpoint['input_conv.weight_v']
       a_						     =			checkpoint['input_conv.bias']

       for i in range(len(config.upsample_rates   )   ):
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_g']
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_v']
              a_						     =			checkpoint[F'upsamples.{i}.1.bias']

       for i in range(len(config.upsample_rates   ) * len(config.resblock_kernel_sizes   )   ):
              for j in range(len(config.resblock_dilation_sizes   )   ):
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.bias']

                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

       a_						     =			checkpoint['output_conv.1.weight_g']
       a_						     =			checkpoint['output_conv.1.weight_v']
       a_						     =			checkpoint['output_conv.1.bias']

       hf_model.remove_weight_norm()


@torch.no_grad()
def       __UpperCamelCase					(      lowercase_       :  List[Any]   ,       lowercase_       :  int   ,       lowercase_       :  Optional[Any]   ,       lowercase_       :  List[str]=None   ,       lowercase_       :  Tuple=None   ,       ):
       """simple docstring"""




       if config_path is not None:
              a_						     =			SpeechTaHifiGanConfig.from_pretrained(lowercase_   )
       else:
              a_						     =			SpeechTaHifiGanConfig()

       a_						     =			SpeechTaHifiGan(lowercase_   )

       a_						     =			torch.load(lowercase_   )
       load_weights(orig_checkpoint['model']['generator']   ,       lowercase_   ,       lowercase_   )

       a_						     =			np.load(lowercase_   )
       a_						     =			stats[0].reshape(-1   )
       a_						     =			stats[1].reshape(-1   )
       a_						     =			torch.from_numpy(lowercase_   ).float()
       a_						     =			torch.from_numpy(lowercase_   ).float()

       model.save_pretrained(lowercase_   )

       if repo_id:
              print('Pushing to the hub...'   )
              model.push_to_hub(lowercase_   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
  parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
  )

  __lowerCAmelCase        =							parser.parse_args()
  convert_hifigan_checkpoint(
      args.checkpoint_path,
      args.stats_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )




